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ppstructure/kie/predict_kie_token_ser_re.py

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
+
+os.environ["FLAGS_allocator_strategy"] = "auto_growth"
+
+import cv2
+import json
+import numpy as np
+import time
+
+import tools.infer.utility as utility
+from tools.infer_kie_token_ser_re import make_input
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.visual import draw_ser_results, draw_re_results
+from ppocr.utils.utility import get_image_file_list, check_and_read
+from ppstructure.utility import parse_args
+from ppstructure.kie.predict_kie_token_ser import SerPredictor
+
+logger = get_logger()
+
+
+class SerRePredictor(object):
+    def __init__(self, args):
+        self.use_visual_backbone = args.use_visual_backbone
+        self.ser_engine = SerPredictor(args)
+        if args.re_model_dir is not None:
+            postprocess_params = {"name": "VQAReTokenLayoutLMPostProcess"}
+            self.postprocess_op = build_post_process(postprocess_params)
+            (
+                self.predictor,
+                self.input_tensor,
+                self.output_tensors,
+                self.config,
+            ) = utility.create_predictor(args, "re", logger)
+        else:
+            self.predictor = None
+
+    def __call__(self, img):
+        starttime = time.time()
+        ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
+        if self.predictor is None:
+            return ser_results, ser_elapse
+
+        re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
+        if self.use_visual_backbone == False:
+            re_input.pop(4)
+        for idx in range(len(self.input_tensor)):
+            self.input_tensor[idx].copy_from_cpu(re_input[idx])
+
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+        preds = dict(
+            loss=outputs[1],
+            pred_relations=outputs[2],
+            hidden_states=outputs[0],
+        )
+
+        post_result = self.postprocess_op(
+            preds, ser_results=ser_results, entity_idx_dict_batch=entity_idx_dict_batch
+        )
+
+        elapse = time.time() - starttime
+        return post_result, elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    ser_re_predictor = SerRePredictor(args)
+    count = 0
+    total_time = 0
+
+    os.makedirs(args.output, exist_ok=True)
+    with open(
+        os.path.join(args.output, "infer.txt"), mode="w", encoding="utf-8"
+    ) as f_w:
+        for image_file in image_file_list:
+            img, flag, _ = check_and_read(image_file)
+            if not flag:
+                img = cv2.imread(image_file)
+                img = img[:, :, ::-1]
+            if img is None:
+                logger.info("error in loading image:{}".format(image_file))
+                continue
+            re_res, elapse = ser_re_predictor(img)
+            re_res = re_res[0]
+
+            res_str = "{}\t{}\n".format(
+                image_file,
+                json.dumps(
+                    {
+                        "ocr_info": re_res,
+                    },
+                    ensure_ascii=False,
+                ),
+            )
+            f_w.write(res_str)
+            if ser_re_predictor.predictor is not None:
+                img_res = draw_re_results(
+                    image_file, re_res, font_path=args.vis_font_path
+                )
+                img_save_path = os.path.join(
+                    args.output,
+                    os.path.splitext(os.path.basename(image_file))[0] + "_ser_re.jpg",
+                )
+            else:
+                img_res = draw_ser_results(
+                    image_file, re_res, font_path=args.vis_font_path
+                )
+                img_save_path = os.path.join(
+                    args.output,
+                    os.path.splitext(os.path.basename(image_file))[0] + "_ser.jpg",
+                )
+
+            cv2.imwrite(img_save_path, img_res)
+            logger.info("save vis result to {}".format(img_save_path))
+            if count > 0:
+                total_time += elapse
+            count += 1
+            logger.info("Predict time of {}: {}".format(image_file, elapse))
+
+
+if __name__ == "__main__":
+    main(parse_args())